Inverse Reinforcement Learning In A Continuous State Space With Formal Guarantees

Abstract

Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior. The IRL setting is remarkably useful for automated control, in situations where the reward function is difficult to specify manually or as a means to extract agent preference. In this work, we provide a new IRL algorithm for the continuous state space setting with unknown transition dynamics by modeling the system using a basis of orthonormal functions. Moreover, we provide a proof of correctness and formal guarantees on the sample and time complexity of our algorithm. Finally, we present synthetic experiments to corroborate our theoretical guarantees.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydexter2021inverse

Related papers

• Towards Theoretical Understanding Of Inverse Reinforcement Learning (2023)0.00
• Inverse Reinforcement Learning Without Reinforcement Learning (2023)0.00
• Maximum-likelihood Inverse Reinforcement Learning With Finite-time Guarantees (2022)0.00
• Inverse Reinforcement Learning With Simultaneous Estimation Of Rewards And Dynamics (2016)0.00
• Active Exploration For Inverse Reinforcement Learning (2022)0.00
• Is Inverse Reinforcement Learning Harder Than Standard Reinforcement Learning? A Theoretical Perspective (2023)0.00
• Inverse Reinforcement Learning With Explicit Policy Estimates (2021)2.26
• A Survey Of Inverse Reinforcement Learning: Challenges, Methods And Progress (2018)0.00